Advantages. The use of generative latent bank is reminiscent of the task of reference-based SR [1]}, [2]}, [3]}, [4]}, [5]}, where external HR reference image(s) are employed as an explicit imagery dictionary.
While the external HR information leads to marked improvements, the performance is sensitive to the similarity between the inputs and references. This sensitivity may eventually lead to degraded results when the reference images/components are not well selected.
Moreover, the size and diversity of those imagery dictionaries are limited by the selected components, impeding the generalization to diverse scenes in practice.
In addition, computationally-intensive global matching [5]} or component detection/selection [1]} is often required to aggregate appropriate information from the references, hindering the applications to scenarios with tight computational constraints.
Instead of constructing an imagery dictionary, GLEAN adopts a GAN-based dictionary conditioned on a pre-trained GAN. Our dictionary does not depend on any specific components or images. Instead, it captures the distribution of the images and has potentially unlimited size and diversity.
Furthermore, GLEAN is computationally efficient without requiring global matching and reference images/components selection.
